Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations683
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)1.2%
Total size in memory58.8 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 8 (1.2%) duplicate rowsDuplicates
Bare Nuclei is highly overall correlated with Bland Chromatin and 7 other fieldsHigh correlation
Bland Chromatin is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Class is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Clump Thickness is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Marginal Adhesion is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 2 other fieldsHigh correlation
Normal Nucleoli is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Single Epithelial Cell Size is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Shape is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Size is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation

Reproduction

Analysis started2024-09-11 17:41:34.123642
Analysis finished2024-09-11 17:41:40.948842
Duration6.83 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Sample code number
Real number (ℝ)

Distinct630
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1076720.2
Minimum63375
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.011884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63375
5-th percentile413929.8
Q1877617
median1171795
Q31238705
95-th percentile1334001.2
Maximum13454352
Range13390977
Interquartile range (IQR)361088

Descriptive statistics

Standard deviation620644.05
Coefficient of variation (CV)0.57642091
Kurtosis257.36841
Mean1076720.2
Median Absolute Deviation (MAD)104296
Skewness13.74841
Sum7.3539992 × 108
Variance3.8519903 × 1011
MonotonicityNot monotonic
2024-09-11T23:11:41.115481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1182404 6
 
0.9%
1276091 5
 
0.7%
1198641 3
 
0.4%
897471 2
 
0.3%
1168736 2
 
0.3%
411453 2
 
0.3%
734111 2
 
0.3%
1293439 2
 
0.3%
560680 2
 
0.3%
1143978 2
 
0.3%
Other values (620) 655
95.9%
ValueCountFrequency (%)
63375 1
0.1%
76389 1
0.1%
95719 1
0.1%
128059 1
0.1%
142932 1
0.1%
144888 1
0.1%
145447 1
0.1%
160296 1
0.1%
167528 1
0.1%
183913 1
0.1%
ValueCountFrequency (%)
13454352 1
0.1%
8233704 1
0.1%
1371920 1
0.1%
1371026 1
0.1%
1369821 1
0.1%
1368882 1
0.1%
1368273 1
0.1%
1368267 1
0.1%
1365328 1
0.1%
1365075 1
0.1%

Clump Thickness
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4421669
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.200044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8207613
Coefficient of variation (CV)0.6349967
Kurtosis-0.63312453
Mean4.4421669
Median Absolute Deviation (MAD)2
Skewness0.58765424
Sum3034
Variance7.9566944
MonotonicityNot monotonic
2024-09-11T23:11:41.274697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 139
20.4%
5 128
18.7%
3 104
15.2%
4 79
11.6%
10 69
10.1%
2 50
 
7.3%
8 44
 
6.4%
6 33
 
4.8%
7 23
 
3.4%
9 14
 
2.0%
ValueCountFrequency (%)
1 139
20.4%
2 50
 
7.3%
3 104
15.2%
4 79
11.6%
5 128
18.7%
6 33
 
4.8%
7 23
 
3.4%
8 44
 
6.4%
9 14
 
2.0%
10 69
10.1%
ValueCountFrequency (%)
10 69
10.1%
9 14
 
2.0%
8 44
 
6.4%
7 23
 
3.4%
6 33
 
4.8%
5 128
18.7%
4 79
11.6%
3 104
15.2%
2 50
 
7.3%
1 139
20.4%

Uniformity of Cell Size
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1508053
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.346831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0651449
Coefficient of variation (CV)0.97281317
Kurtosis0.07367914
Mean3.1508053
Median Absolute Deviation (MAD)0
Skewness1.2264041
Sum2152
Variance9.395113
MonotonicityNot monotonic
2024-09-11T23:11:41.419433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 373
54.6%
10 67
 
9.8%
3 52
 
7.6%
2 45
 
6.6%
4 38
 
5.6%
5 30
 
4.4%
8 28
 
4.1%
6 25
 
3.7%
7 19
 
2.8%
9 6
 
0.9%
ValueCountFrequency (%)
1 373
54.6%
2 45
 
6.6%
3 52
 
7.6%
4 38
 
5.6%
5 30
 
4.4%
6 25
 
3.7%
7 19
 
2.8%
8 28
 
4.1%
9 6
 
0.9%
10 67
 
9.8%
ValueCountFrequency (%)
10 67
 
9.8%
9 6
 
0.9%
8 28
 
4.1%
7 19
 
2.8%
6 25
 
3.7%
5 30
 
4.4%
4 38
 
5.6%
3 52
 
7.6%
2 45
 
6.6%
1 373
54.6%

Uniformity of Cell Shape
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2152269
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.492370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9885808
Coefficient of variation (CV)0.92950851
Kurtosis-0.016815621
Mean3.2152269
Median Absolute Deviation (MAD)0
Skewness1.15789
Sum2196
Variance8.9316153
MonotonicityNot monotonic
2024-09-11T23:11:41.565426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 346
50.7%
10 58
 
8.5%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
7 30
 
4.4%
6 29
 
4.2%
8 27
 
4.0%
9 7
 
1.0%
ValueCountFrequency (%)
1 346
50.7%
2 58
 
8.5%
3 53
 
7.8%
4 43
 
6.3%
5 32
 
4.7%
6 29
 
4.2%
7 30
 
4.4%
8 27
 
4.0%
9 7
 
1.0%
10 58
 
8.5%
ValueCountFrequency (%)
10 58
 
8.5%
9 7
 
1.0%
8 27
 
4.0%
7 30
 
4.4%
6 29
 
4.2%
5 32
 
4.7%
4 43
 
6.3%
3 53
 
7.8%
2 58
 
8.5%
1 346
50.7%

Marginal Adhesion
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8301611
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.638169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8645622
Coefficient of variation (CV)1.0121552
Kurtosis0.94240721
Mean2.8301611
Median Absolute Deviation (MAD)0
Skewness1.5091811
Sum1933
Variance8.2057165
MonotonicityNot monotonic
2024-09-11T23:11:41.707913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 393
57.5%
3 58
 
8.5%
2 58
 
8.5%
10 55
 
8.1%
4 33
 
4.8%
8 25
 
3.7%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
9 4
 
0.6%
ValueCountFrequency (%)
1 393
57.5%
2 58
 
8.5%
3 58
 
8.5%
4 33
 
4.8%
5 23
 
3.4%
6 21
 
3.1%
7 13
 
1.9%
8 25
 
3.7%
9 4
 
0.6%
10 55
 
8.1%
ValueCountFrequency (%)
10 55
 
8.1%
9 4
 
0.6%
8 25
 
3.7%
7 13
 
1.9%
6 21
 
3.1%
5 23
 
3.4%
4 33
 
4.8%
3 58
 
8.5%
2 58
 
8.5%
1 393
57.5%

Single Epithelial Cell Size
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2342606
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.778962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2230855
Coefficient of variation (CV)0.68735508
Kurtosis2.1296393
Mean3.2342606
Median Absolute Deviation (MAD)0
Skewness1.7037164
Sum2209
Variance4.9421089
MonotonicityNot monotonic
2024-09-11T23:11:41.854055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 376
55.1%
3 71
 
10.4%
4 48
 
7.0%
1 44
 
6.4%
6 40
 
5.9%
5 39
 
5.7%
10 31
 
4.5%
8 21
 
3.1%
7 11
 
1.6%
9 2
 
0.3%
ValueCountFrequency (%)
1 44
 
6.4%
2 376
55.1%
3 71
 
10.4%
4 48
 
7.0%
5 39
 
5.7%
6 40
 
5.9%
7 11
 
1.6%
8 21
 
3.1%
9 2
 
0.3%
10 31
 
4.5%
ValueCountFrequency (%)
10 31
 
4.5%
9 2
 
0.3%
8 21
 
3.1%
7 11
 
1.6%
6 40
 
5.9%
5 39
 
5.7%
4 48
 
7.0%
3 71
 
10.4%
2 376
55.1%
1 44
 
6.4%

Bare Nuclei
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5446559
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:41.926746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6438572
Coefficient of variation (CV)1.0279861
Kurtosis-0.79884414
Mean3.5446559
Median Absolute Deviation (MAD)0
Skewness0.99001565
Sum2421
Variance13.277695
MonotonicityNot monotonic
2024-09-11T23:11:41.995108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 402
58.9%
10 132
 
19.3%
2 30
 
4.4%
5 30
 
4.4%
3 28
 
4.1%
8 21
 
3.1%
4 19
 
2.8%
9 9
 
1.3%
7 8
 
1.2%
6 4
 
0.6%
ValueCountFrequency (%)
1 402
58.9%
2 30
 
4.4%
3 28
 
4.1%
4 19
 
2.8%
5 30
 
4.4%
6 4
 
0.6%
7 8
 
1.2%
8 21
 
3.1%
9 9
 
1.3%
10 132
 
19.3%
ValueCountFrequency (%)
10 132
 
19.3%
9 9
 
1.3%
8 21
 
3.1%
7 8
 
1.2%
6 4
 
0.6%
5 30
 
4.4%
4 19
 
2.8%
3 28
 
4.1%
2 30
 
4.4%
1 402
58.9%

Bland Chromatin
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4450952
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:42.064103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4496966
Coefficient of variation (CV)0.7110679
Kurtosis0.16764564
Mean3.4450952
Median Absolute Deviation (MAD)1
Skewness1.0952705
Sum2353
Variance6.0010133
MonotonicityNot monotonic
2024-09-11T23:11:42.208237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 161
23.6%
2 160
23.4%
1 150
22.0%
7 71
10.4%
4 39
 
5.7%
5 34
 
5.0%
8 28
 
4.1%
10 20
 
2.9%
9 11
 
1.6%
6 9
 
1.3%
ValueCountFrequency (%)
1 150
22.0%
2 160
23.4%
3 161
23.6%
4 39
 
5.7%
5 34
 
5.0%
6 9
 
1.3%
7 71
10.4%
8 28
 
4.1%
9 11
 
1.6%
10 20
 
2.9%
ValueCountFrequency (%)
10 20
 
2.9%
9 11
 
1.6%
8 28
 
4.1%
7 71
10.4%
6 9
 
1.3%
5 34
 
5.0%
4 39
 
5.7%
3 161
23.6%
2 160
23.4%
1 150
22.0%

Normal Nucleoli
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8696925
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:42.278709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0526664
Coefficient of variation (CV)1.0637608
Kurtosis0.4735883
Mean2.8696925
Median Absolute Deviation (MAD)0
Skewness1.4204311
Sum1960
Variance9.3187722
MonotonicityNot monotonic
2024-09-11T23:11:42.350740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 432
63.3%
10 60
 
8.8%
3 42
 
6.1%
2 36
 
5.3%
8 23
 
3.4%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
7 16
 
2.3%
9 15
 
2.2%
ValueCountFrequency (%)
1 432
63.3%
2 36
 
5.3%
3 42
 
6.1%
4 18
 
2.6%
5 19
 
2.8%
6 22
 
3.2%
7 16
 
2.3%
8 23
 
3.4%
9 15
 
2.2%
10 60
 
8.8%
ValueCountFrequency (%)
10 60
 
8.8%
9 15
 
2.2%
8 23
 
3.4%
7 16
 
2.3%
6 22
 
3.2%
5 19
 
2.8%
4 18
 
2.6%
3 42
 
6.1%
2 36
 
5.3%
1 432
63.3%

Mitoses
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6032211
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-09-11T23:11:42.417829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7326741
Coefficient of variation (CV)1.0807456
Kurtosis12.273374
Mean1.6032211
Median Absolute Deviation (MAD)0
Skewness3.5114762
Sum1095
Variance3.0021597
MonotonicityNot monotonic
2024-09-11T23:11:42.485362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 563
82.4%
2 35
 
5.1%
3 33
 
4.8%
10 14
 
2.0%
4 12
 
1.8%
7 9
 
1.3%
8 8
 
1.2%
5 6
 
0.9%
6 3
 
0.4%
ValueCountFrequency (%)
1 563
82.4%
2 35
 
5.1%
3 33
 
4.8%
4 12
 
1.8%
5 6
 
0.9%
6 3
 
0.4%
7 9
 
1.3%
8 8
 
1.2%
10 14
 
2.0%
ValueCountFrequency (%)
10 14
 
2.0%
8 8
 
1.2%
7 9
 
1.3%
6 3
 
0.4%
5 6
 
0.9%
4 12
 
1.8%
3 33
 
4.8%
2 35
 
5.1%
1 563
82.4%

Class
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2
444 
4
239 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters683
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Length

2024-09-11T23:11:42.566529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T23:11:42.638735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Most occurring characters

ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 444
65.0%
4 239
35.0%

Interactions

2024-09-11T23:11:40.110805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.257476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.947248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.573008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.263499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.898755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.527742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.156759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.785807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.486659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.181014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.337249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.020627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.643267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.336652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.971585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.598617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.227901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.859584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.555178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.247912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.403988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.080621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.704214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.397791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.032970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.660016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.290742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.921520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.616017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.310083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.471876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.141261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.765454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.460905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.095397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.721247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.354269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.984474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.679460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.372075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.537528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.203504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.829105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.522297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.155351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.784472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.415389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.045616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.742580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.434758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.604470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.264498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.890801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.585162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.217074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.846823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.477592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.105454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.805850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.496637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.673117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.327374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.954957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.648351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.280404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.910861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.540568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.168743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.867743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.557247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.741507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.388656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.015515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.708922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.341480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.972950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.600981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.229103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.927486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.618094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.811519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.449541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.076710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.771505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.404530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.035184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.663747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.369107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.990097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.680068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:34.880813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:35.512126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.204668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:36.837097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:37.466889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.096659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:38.725920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:39.426678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-09-11T23:11:40.050013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-09-11T23:11:42.696708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Bare NucleiBland ChromatinClassClump ThicknessMarginal AdhesionMitosesNormal NucleoliSample code numberSingle Epithelial Cell SizeUniformity of Cell ShapeUniformity of Cell Size
Bare Nuclei1.0000.6790.8390.5910.6970.4740.660-0.1320.6950.7530.770
Bland Chromatin0.6791.0000.8070.5340.6290.3910.662-0.0950.6450.6950.721
Class0.8390.8071.0000.7360.7470.5190.7730.0000.8020.8680.882
Clump Thickness0.5910.5340.7361.0000.5440.4210.566-0.0030.5870.6670.664
Marginal Adhesion0.6970.6290.7470.5441.0000.4470.636-0.0560.6650.7190.745
Mitoses0.4740.3910.5190.4210.4471.0000.510-0.0820.4830.4780.513
Normal Nucleoli0.6600.6620.7730.5660.6360.5101.000-0.0660.7110.7240.753
Sample code number-0.132-0.0950.000-0.003-0.056-0.082-0.0661.000-0.092-0.059-0.041
Single Epithelial Cell Size0.6950.6450.8020.5870.6650.4830.711-0.0921.0000.7650.793
Uniformity of Cell Shape0.7530.6950.8680.6670.7190.4780.724-0.0590.7651.0000.895
Uniformity of Cell Size0.7700.7210.8820.6640.7450.5130.753-0.0410.7930.8951.000

Missing values

2024-09-11T23:11:40.768559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-11T23:11:40.890191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
010000255111213112
1100294554457103212
210154253111223112
310162776881343712
410170234113213112
510171228101087109714
6101809911112103112
710185612121213112
810330782111211152
910330784211212112
Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
6736545461111211182
6746545461113211112
675695091510105454414
6767140393111211112
6777632353111212122
6787767153111321112
6798417692111211112
6808888205101037381024
68189747148643410614
68289747148854510414

Duplicate rows

Most frequently occurring

Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass# duplicates
0320675335231071142
146690611112111122
270409711111121122
3110052461010281073342
4111611691010110833142
5119864131112131122
6121886011111131122
7132194251112131122